Impacts of an Early Education Intervention IFPRIDP Edited Version Final

8/4/2019 Impacts of an Early Education Intervention IFPRIDP Edited Version Final

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Impacts of an Early Education Intervention on Students Learning Achievement:

Evidence from the Philippines1

Futoshi Yamauchi2

Yanyan Liu3

International Food Policy Research Institute

Washington, D.C.

August 2011

1Acknowledgement next page2 International Food Policy Research Institute, 2033 K Street, NW, Washington D.C.; email:[email protected] International Food Policy Research Institute, 2033 K Street, NW, Washington D.C.; email:[email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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Acknowledgments

We would like to thank seminar participants at the Philippine Department of Education and the University of

the Philippines at Los Banos for their useful comments, and the Japan International Cooperation Agency for

financial support. We are most grateful to Yolanda Quijano for generous support and guidance from the onset

of this project, and the Bureau of Elementary Education and various divisions within the department for

collaborations throughout this project, including providing us with various databases for this study. Special

thanks are offered to Juliet Abunyawan and Felisberta Sanchez, who visited former Third Elementary

Education Project (TEEP)division offices to collect TEEP investment data in addition to reorganizing the

Division Education Development Plan database, and Ishidra Abunggol at the Research and Statistics Division,

who provided technical guidance to the first author. We thank Surajit Baruah for his excellent research

assistance in managing the Basic Education Information System database. The TEEP student tracking survey

conducted in eight provinces and the cities Manila, Cebu, and Baguio also offered enormous opportunities for

the authors to extensively visit TEEP and non-TEEP schools and communities, which helped us correctly do

our analysis in this paper. Any remaining errors are ours.


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Abstract

This paper examines the impact of a large supply-side education intervention in the

Philippines, the Third Elementary Education Project, on students national achievement

test scores. We find that the program significantly increased student test scores at grades 4

to 6. The estimate indicates that the six-year exposure to the program increases test scores

by about 15 score points. Interestingly, the mathematics score is more responsive to this

education reform than other subjects. We also find that textbooks, instructional training of

teachers, and new classroom constructions particularly contributed to these outcomes. The

empirical results also imply that early-stage investments improve student performance at

later stages in the elementary school cycle, which suggests that social returns to such an

investment are greater than what the current study demonstrates.

Keywords: School quality, Policy intervention, Elementary schools, Human capital

formation, Philippines


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1. Introduction

Early-stage investments are increasingly recognized as a critical input in human capital

production. These investments in the formation of human capital have dynamic impacts on

outcomes at subsequent stages. Recent literature demonstrates that prenatal and early

childhood nutrition status significantly determines a childs readiness for schooling and

educational and labor market outcomes (Alderman et al. 2001; Alterman, Hoddinott, and

Kinsey, 2006; Maluccio et al. 2009; Yamauchi 2008). The dynamic path of human capital

formation depends on early-stage investments essentially due to the cumulative nature of

its formation (Cunha et al. 2006).

School education is not an exception. For instance, children cannot perform well at higher

grades without sufficient acquisition of knowledge at lower grades. The high rates often

observed of repeating early grades in elementary school show that many children face

difficulty in successfully starting schooling, indirectly proving the importance of initial-

stage investments in determining higher grade performance (Behrman and Deolalikar,

1991). Similarly, successful completion at the elementary school stage is a significant factor

in student performance at the secondary school stage.

This paper assesses the impact of a large-scale intervention to elementary schools, the

Third Elementary Education Project (TEEP), on students learning performance in the

Philippines. The project was implemented by the Philippine Department of Education from

2000 to 2006 with financial assistance from the Japan Bank for International Cooperation

(JBIC) and the World Bank. The unique nature of TEEP was in the combination of physical


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and soft components and institutional reform. Besides investing in physical buildings and

textbooks, TEEP provided training to teachers and principals and introduced school-based

management by partnering school with community. Our study estimates the total impacts

of these investments and reforms on students learning performance, measured by a

change in student test scores during the elementary school cycle, though we expect that

such an intervention has longer term effects beyond this stage, changing their activities in

labor markets.4

Methodologically, we combine double differences with propensity score matching. We

compare the change in test scores before and after the intervention in TEEP-treated

schools with the change in nontreated schools. Propensity score matching is used to reduce

the pre-intervention differences between the treated and nontreated schools. We find that

a two-year exposure to the TEEP intervention significantly increased test scores in grade 4.

Our estimates show that test scores increased by 4 to 5 score points (out of 100) from

grades 4 to 6, which amounts to an increase of about 1215 score points if students are

exposed to the intervention for six years of elementary school education (grades 1 to 6).

We also examine the effects of individual components of TEEP and find that school building

constructions and renovations, instructional training of teachers, and additional textbook

provision significantly increased student test scores. Interestingly, investments in

textbooks for earlier grades have large positive effects on student performance at higher

grades.

4We collect individual and household data from 3,500 students in four TEEP and four non-TEEP divisions to

study long-term impacts of TEEP. This component includes tracking the sample students who migrated out oftheir original communities.


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The paper is organized as follows: The next section describes the program. Sections 3 and 4

discuss data used in our analysis and our estimation method, respectively. Section 5

discloses the average treatment effects. The empirical results are summarized in Section 6.

Section 7 concludes.


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2. Program Background

The Third Elementary Education Project (TEEP) was implemented from 2000 to 2006 by

the Philippine Department of Education in all public primary and elementary schools5 in

the 23 provinces6 identified as the most socially depressed in the Social Reform Agenda.7

The total project cost was US$173.91 million ($91.07 million from JBIC and $82.84 million

from World Bank). The unique feature of TEEP is a combination of investments in school

facility and education materials and school governance reform. Not only were school

facilities and textbook supply improved, but the decisionmaking process was also

decentralized to the school and community levels. TEEP introduced a package of

investments to schools in the selected 23 provinces. Specifically, the package of

investments included (1) school building construction and renovation, (2) textbooks, (3)

teacher training, (4) school-based management, and (5) other facility and equipment

support.

The core of the program is school-based management, through which schools are given an

incentive to manage proactively and more independently of the government. Schools were

5Primary schools cover grades 1 to 4, while elementary schools cover grades 1 to 6.

6The program covered both primary (grades 14) and elementary (grades 16) schools. This paper analyzes the

impacts on only elementary schools. However, converting primary schools to elementary schools by extending

enrollment up to grade 6 was also an important part of the TEEP program. Students who complete primary schools

are likely to attend elementary schools in grades 5 and 6, which changes the student body of those schools betweengrades 14 and grades 5 and 6.7The Ramos administration, along with their medium term development plan, called Philippines 2000,

identified reforms as the key to bridging social gaps and alleviating poverty. The objective of enhancingdevelopment through social reforms led to the formulation of the blueprint for social development in thePhilippines, the Social Reform Agenda (SRA), marked as the first instance of social reforms in the history ofthe Philippines (Ramos 1995). As a result of the initial success of the SRA, the Congress of the Philippines in1998 passed Republic Act 8425, widely known as the Social Reform and Poverty Alleviation Act (Republic ofthe Philippines, Congress, 1998). The law institutionalized the poverty alleviation program and a host ofgrassroots development strategies.


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partnered with communities and parents to decide key issues such as improvement plan

and school finance. Teachers were also trained systematically to improve teaching skills.

Information management is being improved so that schools are responsible for

systematically organizing information on enrollment, learning achievements, finance, and

so forth and reporting it to the division office. Schools are required to set improvement

plans every year and compare them with actual achievement. This dynamic process is

monitored by the division-level education department. School finance is also being

decentralized to some extent to relax the school budget constraints because Philippine

public schools are not allowed to charge school fees. TEEP schools are free to raise their

own funds from communities, parents, and others, though resources are admittedly limited

in many poor communities. These reforms in public schools are expected to improve

education quality, which would then in turn increase returns to schooling in labor markets

(see Yamauchi 2005, on returns to schooling).

The selection of TEEP provinces was purposive because it intended to cover the most depressed

provinces identified in the Social Reform Agenda. TEEP allocation is rather different in the

Philippines three macroregions. As shown in Figure 2.1, in the northern macroregion of Luzon,

TEEP was concentrated in the Cordillera Administrative Region, a mountainous region in the

center of northern Luzon. In the central macroregion of Visayas, TEEP divisions were relatively

evenly distributed. In the southern Mindanao macroregion, TEEP divisions were clustered,

though not as clustered as in northern Luzon.

Figure 2.1 to be inserted


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TEEP was initially designed to follow a phase-in plan with three batches at the province

level. However, the plan was altered in practice due to variations in preparedness across

divisions. Because understanding the implementation process of TEEP is important in

choosing the appropriate strategy to identify the TEEP impacts, we collected school-level

data on program implementation time and investment amounts of different components.

The data confirm that actual implementation did not follow the batch plan and suggest that

the first and second batches were implemented almost simultaneously.8 We will describe

TEEP implementation in more detail in the data section.

8Khattri, Ling, and Jha (2010) used the lag between the first and second batches to identify the effect of

school-based management on student test scores. Their analysis also includes TEEP investments such as newconstructions as exogenous controlling variables. Their identification strategy is questionable given that, inreality, the initial phase plan was changed due to variations in preparedness across divisions.


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3. Data

This section describes the data used in our analysis. We combine the official test and school

databases and the investment data that we collected in the (TEEP) divisions. For test scores

and school conditions at the start of the project, we use the National Achievement Test

(NAT) score data and the Basic Education Information System (BEIS) data, respectively.

The NAT data provide average test scores for grade 4 students in school year (SY) 2002/03,

grade 5 in SY 2003/04, and grade 6 in SY 2004/05 for each school. We note that grade 4 in

SY 2002/03, grade 5 in SY 2003/04, and grade 6 in SY 2004/05 constitute panel data that

tracked the same cohort in each school.

Table 3.1 to be inserted

Table 3.1 shows the mean and standard deviation of mathematics and overall scores of the

cohort in SY 2002/03 and SY 2004/05 for TEEP and non-TEEP areas, separately. TEEP

schools have higher average scores than non-TEEP schools in both years.

The BEIS data provide detailed information on student enrollment and achievements and

teachers since SY 2002/03. The data normally disaggregate the information by grade, age,

and gender.9

9 BEIS data needed intensive programming to transform for analysis. The data were originally in MicrosoftExcel. The computer program needed about 10 hours to reorganize school-level data in different divisionsand regions for one school year.


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We obtain income data on municipalities (or school district) from the 2000 Census. Local

income level is an important factor that determines school and family environments.

Controlling local income levels is crucial because competition between public and private

schools matters in the selection of students in the Philippine context. In high-income

municipalities (school districts), students from well-off families and with high test scores

are likely to be accepted into private schools. Therefore, we expect differences in the ability

distribution in public schools between high- and low-income municipalities. If school

quality and student ability are complementary, the effect of TEEP on NAT change is

expected to be different between high- and low-income districts.

We assigned an income category to each school district based on the 2000 Census. The

census defined income category (ranking from 1, highest, to 6, lowest) for each

municipality.10 Note that some municipalities are split into a few school districts. In cities,

we ranked school districts as 1 based on the income threshold used for municipalities.

TEEP was implemented not randomly but in the divisions identified as socially most

depressed in the presidential Social Reform Agenda. Figure 3.1 shows the distribution of

school districts by income category in TEEP and non-TEEP groups. School districts are

concentrated in income categories 1, 4, and 5that is, the highest income and the two

lowest income rankingsfor both TEEP and non-TEEP. Though we observe that more

10 The income classification of municipalities (municipality income) used in this paper is based on Republic of

the Philippines, Department of Finance (2001), Department Order No. 32-01 (effective November 20, 2001)

and Census 2000. The income categories for 1,435 municipalities are defined as follows: 1: Philippine peso

(PHP) 35 million (M) or more (number of municipalities: 130); 2: PHP 27M or more but less than PHP 35M

(140); 3: PHP 21M or more but less than PHP 27M (204); 4: PHP 13M or more but less than PHP 21M (543);

5: PHP 7M or more but less than PHP 13M (401); 6: less than PHP 7M (17).


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school districts are in income category 4 (and fewer in 1) in the TEEP group than in the

non-TEEP group, the difference does not look significant. Further, Figure 3.2 shows the

distribution of schools in the TEEP and non-TEEP groups. Our basic observation remains

valid here. Therefore, it is likely that we can find (and compare) school districts that share

similar socioeconomic conditions in both TEEP and non-TEEP divisions.

Figures 3.1 and 3.2 to be inserted

For TEEP implementation information, we have the Division Education Development Plan

data, which was part of the TEEP completion reports. This dataset has aggregated TEEP

inputs during SY 2000/01 to SY 2004/05. However, it does not identify implementation

timing and inputs of different components of TEEP. Furthermore, the completeness and

quality of the data substantially vary across divisions. To overcome this gap in the data, we

visited 23 TEEP division offices to find the raw data on TEEP investments. The raw data we

collected reveal details of different TEEP investments: textbooks, training, school-based

management, school building, school innovation and improvement fund,

equipment/furniture, and supplementary instructional materials. For training, we

identified the starting date of teacher training and calculated the total number of man-

hours spent in training during SY 2000/01 to SY 2004/05 by different categories. For

textbooks, we identified investment amounts (quantity and cost by grade and subject) in

each school year. Similarly, we sorted school building projects by completion year and

identified new construction and renovation cases and their aggregate total values by school.


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Table 3.2 describes the initial implementation timing of different TEEP components: school

building new construction and renovation, textbooks, and teacher training. The table shows the

percentage of schools covered under TEEP in Visayas (our analysis is restricted to this area)

from SY 2000/01 through SY 2005/06. In school buildings, we aggregated new construction and

renovation projects by their completion timings. In textbooks, we used timing in which textbooks

(disaggregated by grade and subject) were distributed to schools. In teacher training, we only

used the initial time when training was introduced. Note that training covers a wide range of

contents, which principals and teachers studied step by step. In many cases, training was

conducted at the school district level. This means that instructors visit districts one by one within

a division, and therefore it took them a few years to cover all the topics (our data show only total

man-hours and the start date). The table shows that by SY 2002/03, about 80 percent of schools

had received textbooks and 50 percent had at least one completed school building project. In all

schools, the training process had just begun.


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4. Estimation Method

Because the original phase-in plan of TEEP was not followed in practice, we cannot explore

the pipeline design to identify the impact of TEEP on school performance. Therefore, we

formed a control group based on the schools in the non-TEEP provinces to estimate the

counterfactual of the treatment group, which are the schools in the TEEP provinces. Double

differences (DD) based on the cohort panel from grade 4 (SY 2002/03) and grade 6 (SY

2004/05) is used to eliminate cohort-specific fixed effects.11

Because the allocation of TEEP was purposive, the initial school conditions are likely to

have different distributions in the treatment and control groups. If the initial conditions

affect subsequent changes of the outcome variables, DD would give a biased estimate of the

TEEP impacts. We use two strategies to deal with the potential bias due to nonrandom

program placement. First, we use the sample from Visayas only. As shown in Figure 2.1,

TEEP divisions are relatively evenly distributed throughout Visayas compared with the

other two macroregions. We therefore expect that the TEEP and non-TEEP provinces are

more comparable in Visayas, and hence our extra data collection and cleaning efforts were

focused on Visayas. Second, we use propensity score (PS) matching to balance observable

cohort characteristics and initial conditions between the treated and the control groups.

Three caveats exist in our method. First, our baseline is not free of contamination. Table 3.1

showed that TEEP had been implemented in all treated schools by SY 2002/03. Thus, the

initial level of test scores in the treatment group reflects earlier investments completed

11Due to delayed preparations at the early stage of TEEP, most of the program schools received investments during

or after SY 2002/03.


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before SY 2002/03. Second, it is possible that students from primary schools, which are not

part of our sample, came into grades 5 and 6 in our sample elementary schools, which

alters the student body at grade 5. Since TEEP also contributed to the conversion of

primary schools to elementary schools by building new classrooms and staffing for grades

5 and 6, it is possible that attrition is different in the treated and control groups.12 Third, as

an observational analysis, we cannot eliminate bias due to time-variant unobservables.

To illustrate our empirical approach, let if a cohort is treated (located in TEEP area)

and if a cohort is not treated (located in non-TEEP area). Let the outcome of being

treated by TEEP and the counterfactual outcome at time be denoted by

. The gain

from treatment is

, and we are interested in the average effect of treatment on

the treated (ATET),

. With denoting SY 2004/05 and

denoting SY 2002/03, we can write the standard DD estimator as

where is the selection bias and

. If the selection bias is

constant over time ( ), the DD estimator yields an unbiased estimate of the actual

program impact.

The condition or

will not hold if the cohort

characteristics or initial conditions affect subsequent changes of the outcome variables and

have different distributions in the treatment and control groups. To account for this, we use

12In SY 2002/03, total grade 5 enrollment was 94.1 percent of the total grade 4 enrollment in TEEP schools

on average, compared with 95.4 percent in non-TEEP schools; and the total grade 6 enrollment was 94.6percent of the total grade 5 enrollment in TEEP schools on average, compared with 95.5 percent in non-TEEPschools.

1D

0D

t


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PS matching to balance cohort characteristics and initial conditions. The assumption

underlying PS matching is that, conditional on observables,, the outcome change if not

treated is independent of the actual treatment; that is, [

]. This has been

shown to imply [

], where is the propensity score, defined as

(Rosenbaum and Rubin 1983).

We use a PS-matched kernel method and a PS-weighted regression method (Hirano,

Imbens, and Ridder 2003). The PS-matched method estimates

,/)( 101

NYWY jijD

i

D ji

(1)

where 1N is the number of treated villages and ijW is the weight corresponding to villages i

(treated) andj(untreated); and

,]/))()([(/]/))()([(0

nik

D

nijij bXPXPGbXPXPGWk

(2)

where (.)G is a kernel function andn

b is a bandwidth parameter. We use bootstrapping

with 100 replications to estimate the standard errors for the PS-matched kernel method.

We choose the PS-matched kernel method instead of the more commonly used nearest-

neighbor matching to obtain valid bootstrapped standard errors (Abadie and Imbens

2006a, 2006b).

)(XP

)|1Pr()( XDXP


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The PS-weighted method recovers an estimate of the ATET as the parameter in a

weighted least square regression of the form

, (3)

where weights equal 1 for treated and )](1/[)( XPXP for nontreated observations. See

Chen, Mu, and Ravallion (2009) for empirical applications of these two methods.

Since ATET can be estimated consistently only in the common support region of X, the

choice of trimming method is important. We follow Crumpet al. (2009) to determine the

common support region by

)(|10 XPXA , (4)

where 1 if

,1|)(1

1

2)(1

1

sup

DXPEXPX (5)

and otherwise solves

)(,1|

)(1

12

1

1XPD

XPE

.

(6)

This method minimizes the variance of the estimated ATET.


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5. Average Treatment Effects

In the estimation, we merged NAT grade 4 in SY 2002/03 and NAT grade 6 in SY 2004/05

using elementary schools in SY 2002/03.13 Although the selection of TEEP is based on

province-level poverty indicators summarized in the Social Reform Agenda, we conjecture

that income distributions overlap between TEEP and non-TEEP school districts (see

Figures 3.1 and 3.2). In our matching estimation, we control for the interactions of

municipality income category and regional dummies, as well as school-level initial

conditions including pupilteacher ratio, grade 4 total enrollment, number of multigrade

classes, and proportion of locally funded teachers. In the Philippine context, local income

level not only summarizes broad socioeconomic factors but also proxies the availability of

private schools, which affects the competition between public and private schools and

therefore the ability distribution of students in public schools (see, for example, Yamauchi

2005). It also controls local labor market conditions.

The first-stage logit regression result is reported in Table 5.1. The dependent variable is 1 if

the school is located in a TEEP area and zero otherwise. The results show that income

categories, distinguished by regions, significantly explain TEEP placement. Except for

income category 5, which is the poorest group, the effect is monotonic. In region 7, central

Visayas, which is omitted as the benchmark case, the effect of income category 5 is

negative. In other regions, western and eastern Visayas, the income effect is monotonic

throughout all income classes.

13Our analysis pertains only to elementary schools in SY 2002/03, which offered grades 1 to 6. To maintain avalid cohort, we dropped primary schools, where only grades 1 to 4 are taught.


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The pseudo R-squared of the logit regression is 0.22, which suggests plausible explanatory

power.The PS of each observation is estimated based on the regression. Figure A.1, in the

Appendix, plots densities of the estimated PS in the treatment and control groups as well as

the cut-point of the PS values above which observations are trimmed. To illustrate the

effects of trimming and reweighting, Table A.1 displays simple differences of the

explanatory variables between the treatment and control groups in the untrimmed sample

and the PS weighted and trimmed samples. Although simple differences between the

groups are large and statistically significant in the untrimmed sample, trimming and

matching based on the propensity score eliminates all significant differences.


In Table 5.2, we report the estimation results on ATET of TEEP. We examine changes in

overall and mathematics NAT scores from grade 4 in SY 2002/03 to grade 6 in SY

2004/05.14 Panel 1 shows the simple DD results for the overall test and mathematics test

scores. The effects on both scores are small in magnitude and insignificant statistically.

Panels 2 and 3 show the results using DD and PS matching (weighted regression) and DD

and PS matching (kernel), respectively. The two methods give close results, which suggests

14Mathematics is the only common subject that was tested by all schools in the two grades. Overall score is

the summation of scores of all the subjects being tested.


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that TEEP has significant impacts on both overall and mathematics scores. The magnitude

is about 4 overall and 5 for mathematics. In other words, TEEP attributes to an increase of

about 6 percent in the overall test score and 8 percent in the mathematics score on

average.15 The impact is not trivial over the two-year period. If the impact can continue at

the same rate, the total effect of TEEP over six years (if students are exposed to TEEP in the

entire elementary school period) would be a score increase of about 12 to 15 points. This

magnitude of performance improvement is substantial. We note that the DD and PS

matching estimates of the TEEP impacts are larger than the simple DD estimates, which

implies that the endogenous allocation of TEEP creates downward bias in the estimates if

the program allocation is not taken into account. That is, it is likely that TEEP schools (and

school districts) would tend to have a lower trend in NAT than non-TEEP schools if TEEP

were not in place.

15 This is computed by dividing the estimated ATET of TEEP by the counterfactual average score of thetrimmed treatment group in SY 2004/05.


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6. Componentwise Analysis

The previous analysis suggests that TEEP, as a whole, has a significant effect on school

performance. Because TEEP is a combination of several components, in this section we

explore how each component contributes to school performance. To do so, we specify the

empirical model as

,

where is the change in human capital (measured by test scores) from SY 2002/03 to SY

2004/05 . , , and are TEEP investments in textbooks,

teacher training, and building, respectively, that are expected to benefit the cohort under

study.16 Investments in textbooks include those for grades 4, 5, and 6 separately.

Investments in training include instruction training and subjective training of teacher.

Investments in building refer to the number of new school constructions and new

renovations.zis a vector of the initial district- and school-level conditions including the

interactions of municipality-level income categories and regional dummies, pupilteacher

ratio, grade 4 enrollment, number of multigrade classes, and proportion of local funded

teachers. We note that the initial human capital and TEEP investments are potentially

complementary (and thus not separable), but we assume that the initial school conditions

are sufficient to control such heterogeneities in the intervention effect.


16For example,grade 4 textbookrefers to the textbooks distributed to grade 4 in SY 2002/03. The grade 4

textbook distributed to grade 4 in SY 2003/04 is not counted because it did not benefit our cohort.


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The results are presented in Table 6.1, both for the entire sample and for the TEEP-only

sample. The findings are summarized as follows: First, in the textbook effect, earlier stage

investments seem very important in determining later stage outcomes. Grade 4 textbook

affects student outcomes from grade 4 to grade 6 onward. This finding is consistent with

the recently well established view on the cumulative process of human capital

accumulation. Second, new classroom construction significantly helps improve their

performance. The effect of renovations is also significant, although it has a much lower

magnitude. Third, instructional training seems to have a greater positive effect on student

performance than subjectwise training (mathematics, English, and so forth). The latter has

a negative effect on student performance, at least in the short run, probably because

teachers have to use their teaching time to receive training.

This analysis has some reservations. First, since our sample students (cohorts) are at grade

4 in SY 2002/03, we focus on textbooks for grades 4 to 6 distributed at TEEP. These

students (cohorts) could have used TEEP textbooks at lower grades, but the impacts of the

textbooks are already reflected in their NAT scores at SY 2002/03 (grade 4). Second,

though we have information on school building project contract values, we use the number

of new constructions and renovations because the contract value aggregates both types

and we also conjecture that the impacts are different between new constructions and

renovations. These conjectures were supported in preliminary analyses.

Finally, in this study, we did not explicitly assess school-based management, mainly

because we did not find appropriate input measures and variations. The batch plan was not


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strictly implemented especially in the first and second batch groups (that is, they were

mixed in reality, depending on the updated preparedness at the division level). This soft

component is thought to improve the overall effectiveness of physical investments and

teacher training.


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7. Conclusion

This paper provided evidence from the Philippines that both physical and soft components

of public school education investments significantly increased student test scores, by about

1215 score points in the National Achievement Test (NAT) with the six-year exposure.

Our study also showed that the performance in mathematics is more positively responsive

to education reform and investments than other subjects.

Second, we also found evidence that early-stage investments improve student performance

at later stages in the elementary school cycle. The distribution of grade 4 textbooks is

shown to increase subsequent student test scores more than grade 5 or grade 6 textbooks

do. This is not surprising due to the cumulative nature of knowledge acquisition (not just in

education), but this dynamic production cannot be identified without exogenous variations

in the inputs. Our results imply that improved educational quality at the elementary school

stage has positive impacts on educational progress at later stages.

The above findings, when combined with evidence in the literature, imply that public

investments in elementary education likely have positive longer term impacts on education

performance at the subsequent stages: for example, progression to high schools and

colleges and academic performance. If so, social returns to an early-stage investment can

be greater than what the current study seems to show. This argument justifies large public

investments to improve school quality at the early stage of public education, because the

cumulative benefits are gradually realized at later stages in the education system and labor

markets.


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The competition between public and private schools is a unique feature of the Philippine

education system due to the historical dominance of private institutions. In this context,

some studies support an ability-screening hypothesis that private schools screen high-

ability students but their actual schooling investments are not contributing to productivity

increase (see, for example, Yamauchi 2005). The ability screening with the privatepublic

competition, given high costs of private schools, is socially inefficient. If publicly subsidized

and high-quality education is available, we also expect the inflow of good students into the

public school system in the long run.


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Table 3.1Summary of NAT test scores for TEEP and non-TEEP schools, SY 2002/03 and SY 2004/05

TEEP Non-TEEP

SY 2002/03 SY 2004/05 SY 2002/03 SY 2004/05

Mean s.d. Mean s.d. Mean s.d. Mean s.d.

Overall score 46.975 14.674 63.712 13.431 44.447 13.515 59.795 12.875Math score 48.390 17.961 66.035 16.624 45.823 16.753 62.208 16.698

Number of

observations 1,774 1,774 2,434 2,434

Source: National Achievement Test database, various years.

Note: s.d. = standard deviation.

Table 3.2Percentage of TEEP schools in the Visayas region by the initial implementation

timing

SY

2000/01

SY

2001/02

SY

2002/03

SY

2003/04

SY

2004/05

SY

2005/06

New construction and renovation projects 6% 22% 49% 63% 84% 86%

Grade 1 textbook distribution 76% 76% 81% 100% 100% 100%






Training program of teachers 31% 99% 100% 100% 100% 100%

Source: TEEP investment database (the authors survey ), and Division Education Development Plan

database


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Table 5.1Logit estimation of TEEP placement

TEEP Coefficient

Standard

Error Significance

Region 6 2.161 0.211 ***

Region 8 2.518 0.226 ***Income 2 1.341 0.308 ***

Income 3 1.702 0.370 ***

Income 4 0.306 0.190

Income 5 0.141 0.186

Region 6 Income 2 1.337 0.419 ***

Region 6 Income 3 1.097 0.425 ***

Region 6 Income 4 0.330 0.259

Region 6 Income 5 1.980 0.388 ***

Region 8 Income 2 0.784 0.397 **

Region 8 Income 3 0.911 0.426 **

Region 8 Income 4 1.325 0.264 ***

Region 8 Income 5 0.954 0.312 ***

Pupilteacher ratio (both local and

national) 0.008 0.004 *

Grade 4 total enrollment (in ages 6 to 11) 0.008 0.001 ***

Number of multigrade classes 0.042 0.040

Proportion of local funded teachers 0.203 0.596

Constant 1.304 0.212 ***

Number of observations 4208

Pseudo R2 0.22

Source: National Achievement Test database, TEEP investment database (the authors survey ),

Division Education Development Plan databse, Basic Education Information System database, Census

2000 Municipality Income Classifications

Note: *** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.


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Table 5.2Impacts of TEEP on school performance

Untrimmed sample, simple DD

Treated diff Control diff DD se sig.

Overall score 16.737 15.348 1.389 0.874

Math score 17.645 16.385 1.260 1.090Number of

observations 1,774 2,434

Trimmed sample, DD+PS weighted regression


Overall score 16.074 12.139 3.934 1.129 ***

Math score 16.961 11.719 5.242 1.473 ***

Number of


Trimmed sample, DD+PS weighted kernel


Overall score 16.074 12.260 3.813 1.172 ***

Math score 16.961 11.961 5.000 1.442 ***

Number of


Source: National Achievement Test database, TEEP investment database (the authors survey ), Division

Education Development Plan databse, Basic Education Information System database, Census 2000

Municipality Income Classifications

Note: *** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.


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Table 6.1Estimation results of component analysis, dependent variables being change in

mathematics score and overall score

Mathematics Score Overall Score

All sample TEEP only All sample TEEP only

Grade 4 textbooks(peso/pupil) 0.042*** (0.007) 0.015** (0.006) 0.034*** (0.005) 0.014*** (0

Grade 5 textbooks

(peso/pupil) 0.007 (0.005) 0.000 (0.005) 0.005 (0.004) 0.001 (0

Grade 6 textbooks

(peso/pupil) 0.003 (0.005) 0.002 (0.005) 0.004 (0.004) 0.003 (0

Instructional training (man-

hours/pupil) 0.475** (0.227) 0.323* (0.188) 0.417** (0.176) 0.262* (0

Subject training (man-

hours/pupil) 0.845** (0.325) 0.583* (0.301) 0.614** (0.258) 0.401 (0

New constructions (number

in SY 2003/04) 5.785*** (1.917) 5.359*** (1.968) 5.418*** (1.104) 5.042*** (1

New renovations (number in

SY 2003/04) 1.513*** (0.473) 1.214** (0.489) 1.139*** (0.331) 0.895** (0

Region 6 7.179** (3.264) 3.530 (3.989) 3.206 (2.722) 3.095 (3

Region 8 0.548 (3.398) 19.31 (3.341)) 0.200 (2.786)

14.11*** (2

Income 2 4.607 (3.662) 2.908 (3.976) 4.394 (3.132) 2.587 (3

Income 3 2.813 (3.383) 3.687 (3.410) 1.825 (2.766) 2.330 (2

Income 4 0.665 (3.297) 0.951 (3.510) 1.036 (2.677) 1.512 (2

Income 5 2.156 (2.967) 1.157 (3.154) 1.433 (2.449) 0.764 (2

Region 6 Income 2 1.959 (4.332) 2.931 (5.158) 1.040 (3.775) 4.883 (5

Region 6 Income 3 0.244 (4.558) 0.999 (4.862) 0.074 (3.715) 0.842 (4Region 6 Income 4 0.399 (4.019) 4.303 (5.442) 0.711 (3.246) 3.668 (4

Region 6 Income 5 0.050 (3.697) 0.525 (5.500) 0.361 (3.132) 1.261 (4

Region 8 Income 2 1.071 (4.713) 8.097 (3.929) 0.273 (3.988) 6.017 (3

Region 8 Income 3 2.603 (4.172) 17.914 (4.981) 1.831 (3.351) 12.65*** (4

Region 8 Income 4 0.785 (3.990) 13.628 (4.421) 2.081 (3.238) 11.89*** (3

Region 8 Income 5 2.174 (4.486) 10.673 (4.080) 2.523 (3.533) 9.84*** (3

Pupil teacher ratio 0.117** (0.049) 0.126 (0.076) 0.098** (0.040) 0.155** (0

Grade 4 total enrollment 0.048 (0.010) 0.058 (0.018) 0.047*** (0.008) 0.061*** (0

Number of multi-grade

classes 0.441 (0.373) 0.116 (0.604) 0.487* (0.283) 0.161 (0

Proportion of local funded

teachers 11.855* (6.805) 6.273 (14.301) 8.36 (5.56) 9.54 (1

Constant 15.40*** (3.292) 21.38*** (3.694) 15.11*** (2.66) 20.76*** (3

Number of observations 3891 1471 3891 1471

R-squared 0.061 0.089 0.062 0.114


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Education Development Plan database, Basic Education Information System database, Census 2000


Note: Pesos are in Philippine pesos, PHP. Standard errors are in parentheses. *** significant at the 1%

level, ** significant at the 5% level, * significant at the 10% level


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Figure 2.1Map of TEEP and non-TEEP divisions in Philippines (TEEP areas are in red)

Source: The authors calculation


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Figure 3.1Histogram of school districts by income category for TEEP and non-TEEP

groups

Source: Census 2000 Municipality Income Classifications

Figure 3.2Histogram of sampled schools by income category for TEEP and non-TEEPgroups

Non TEEP

1 5

.396739

TEEP

1 5

Non TEEP

1 5

.402526

TEEP

1 5


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Source: Census 2000 Municipality Income Classifications


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Appendix

Table A.1Balance check

Untrimmed sample Trimmed sample Trimmed sample

Simple DD DD+PS weightedregression DD+PS weighted kernel

Diff. s.e. Sig. diff. s.e. sig. diff3 se3 sig3

Region 6 0.287 0.047 *** 0.004 0.046 0.010 0.046

Region 8 0.144 0.050 *** 0.000 0.055 0.003 0.057

Income 2 0.012 0.032 0.002 0.017 0.004 0.022

Income 3 0.012 0.040 0.000 0.035 0.004 0.034

Income 4 0.108 0.050 ** 0.004 0.062 0.022 0.060

Income 5 0.021 0.039 0.001 0.054 0.000 0.041

Region 6 Income 2 0.024 0.015 0.000 0.010 0.002 0.011

Region 6 Income 3 0.026 0.026 0.001 0.025 0.002 0.028

Region 6 Income 4 0.048 0.033 0.002 0.032 0.001 0.038

Region 6 Income 5 0.101 0.020 *** 0.000 0.005 0.002 0.005

Region 8 Income 2 0.032 0.019 * 0.000 0.014 0.004 0.014

Region 8 Income 3 0.041 0.027 0.000 0.025 0.003 0.027

Region 8 Income 4 0.026 0.038 0.001 0.047 0.003 0.044

Region 8 Income 5 0.008 0.014 0.001 0.014 0.004 0.014

Pupilteacher ratio 2.254 0.758 *** 1.101 0.847 1.306 0.930

Grade 4 total enrollment 7.475 1.325 *** 0.687 1.198 0.511 1.257

Number of multi-grade

classes 0.134 0.050 *** 0.037 0.077 0.038 0.090

Proportion of local fundedteachers 0.005 0.003 0.001 0.004 0.000 0.004

Number of observations 4208 3949 3949


Education Development Plan datase, Basic Education Information System database, Census 2000


Note: DD: Double difference, PS: Propensity score, se: Standard errors, diff: mean-difference, ***

significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.


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Figure A.1Plot of estimated propensity scores for schools in non-TEEP and TEEP areas

Source: National Achievement Test database, TEEP investment database (the authors survey ),

Division Education Development Plan database, Basic Education Information System database, Census

2000 Municipality Income Classifications

Impacts of an Early Education Intervention IFPRIDP Edited Version Final

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